5 advanced techniques to make your data AI-ready

From retail to manufacturing, and from financial services to healthcare, every industry is eager to capitalize on the potential of artificial intelligence. But AI-ready data is essential to realizing that promise.

Download our latest white paper to explore advanced techniques for making your data AI-ready, and learn why ongoing commitment to metadata management, data observability, and knowledge graphs is essential for business and tech leaders aiming to operationalize data-centric AI with confidence and control. Below we give you some thought starters, where the white paper provides more in-depth details and strategies to put your best data-centric AI foot forward, faster.

What does it mean to have AI-ready data?

Unlike traditional data management, data-centric AI comes with its own set of demands. AI-ready data must fully represent the use case at hand, including expected patterns, edge cases, errors, outliers, and anomalies. It must also be structured, labeled, trustworthy, and accessible to deliver relevant results for the intended outcome. This reflects the growing maturity of the semantic layer, which provides the contextual data and governance needed to support data-centric AI practices.

1. Data-centric AI

Organizations must balance data-centric AI and model-centric AI approaches, rather than increasingly focusing on the latter, because by 2026, 60% of AI projects are expected to fail due to poor data quality. Data-centric AI focuses on improving data quality, structure, and labeling so models can deliver accurate, reliable, and explainable outcomes, leading to better generalization in real-world applications.

This approach is especially effective in highly regulated, low-data, high-stakes environments like healthcare, finance, and manufacturing, where quality matters more than quantity. It leads to less brittle models, fewer blind spots, smoother compliance, and faster development cycles.

Here are some data-centric AI use cases across industries:

• Medical imaging: Well-annotated scans can help AI detect early signs of cancer or stroke with fewer false positives. 
• Manufacturing: Clean sensor data labeled for anomalies like vibration spikes enables accurate predictive maintenance. 
• Finance: Labeled and enriched transaction data sharpens fraud detection. and reduces false alarms.

2. Active metadata management & data observability

As AI adoption becomes more pervasive across the enterprise, the ability to trust and trace your data becomes non-negotiable. Active metadata management and data observability form the backbone of trustworthy data-centric AI, ensuring transparency and responsible use of data pipelines.

Metadata delivers the context, visibility, and control needed to govern data at scale. Data observability complements this with real-time data quality assessment to detect anomalies and track schema changes. This is especially valuable in complex environments with siloed data and fragmented systems. When you can’t trace lineage or spot schema drift in real-time, AI models become harder to trust and even harder to scale.

Grid Dynamics’ Data Observability Starter Kit makes it easy to monitor pipeline health across structured and unstructured data using rule-based and ML-driven checks.

Here are some metadata management use cases across industries: 

• Retail: Metadata tracks the origin and transformation of product information across systems for accurate pricing and recommendations.
• Healthcare: Observability alerts teams to missing or delayed patient data in real time, helping avoid errors in AI-powered diagnostics or care recommendations.
• Finance: Metadata logs access and lineage for full regulatory compliance and audit readiness by reducing unauthorized access and data drift.

3. Small and wide data

Not every data-centric AI use case has the luxury of massive datasets. In many regulated or specialized domains, data is limited, scattered, or exists in multiple formats. Small and wide data techniques help you unlock value from what you already have, so you don’t have to wait for massive datasets to get started.

Small data focuses on clarity and precision using lean, high-quality datasets. Wide data blends structured, unstructured, and real-time sources, giving AI richer context and broader insight. Together, they enable faster, more explainable results in environments with real-world constraints, such as data sensitivity, storage limits, or access controls.

These techniques are ideal for industries like healthcare, finance, legal, and aerospace, where privacy constraints, sample size limits, or compliance requirements make traditional big data approaches impractical. However, advanced techniques help stretch limited data.

• Transfer learning: Adapt large models to your specific domain with minimal training data.
• Few-shot learning: Train models with just a handful of examples—ideal for rare events.
• Hybrid modeling: Combine diverse data types (text, images, time series) to enhance accuracy.

Here are some small and wide data use cases across industries: 

• Aerospace: Detect anomalies in satellite operations with few-shot learning, where failure data is rare and costly to obtain. 
• Legal tech: Combine structured metadata, such as case type and jurisdiction, to assess case risks and make more informed decisions with limited historical data.
• Healthcare: Apply transfer learning to adapt large-scale models to hospital-specific or specialty-specific datasets for personalized care with minimal patient records.

4. Synthetic data

Sometimes, the data you need just isn’t there. It may be too rare, too sensitive, or too biased to use. Synthetic data mimics real-world patterns and structure, enabling safe, scalable AI development without the risks of exposing or relying solely on real data. It solves three major issues: data scarcity, privacy concerns, and biased datasets, making it especially useful when dealing with private medical records, hard-to-find fraud scenarios, or customer interactions you can’t legally share.

To create high-quality synthetic datasets, organizations rely on a range of generation methods, including rule-based generation, GANs, and diffusion models.

Here are some synthetic data use cases across industries:

• Healthcare: Create realistic medical images to support early detection without relying on sensitive patient data or waiting years to collect enough real examples.
• Finance: Simulate high-risk transaction patterns to train and validate fraud detection systems without exposing real customer records or needing unfounded historical cases.
• Customer service: Train chat tools with synthetic conversations that reflect typical customer queries, edge cases, and emotional tone without breaching data privacy regulations.

5. Knowledge graphs

To augment human intelligence, AI needs context. Knowledge graphs bring structure and meaning to scattered data by connecting entities like people, products, and events through real-world relationships to reason, explain, and surface connections. This makes them ideal for high-stakes, data-rich environments where explainability and traceability are key.

Whether you’re mapping court cases in legal tech, tracking drug trials in pharma, or modeling customer intent across channels, knowledge graphs enable human-understandable logic paths that break down silos, help systems reason instead of just react. They also support better data reuse across teams and simplify compliance by exposing how and why an output was generated.

Organizations must combine smart data extraction with intelligent relationship modeling techniques—entity extraction, relationship mapping, and graph reasoning—to bring structure, context, and reasoning to otherwise disconnected information.

Here are some knowledge graph use cases across industries:

• Legal tech: Speed up legal research with connected rulings and precedents that would otherwise be buried across thousands of disconnected documents.
• Pharma R&D: Connect molecular data, gene targets, trial outcomes, and published research into a navigable network to accelerate drug discovery.
• Customer intelligence: Build real-time profiles by unifying purchase history, support tickets, website behavior, and past interactions to drive personalized experiences.

Choose the right data-centric AI techniques for your domain maturity

The right data-centric AI techniques depend heavily on your industry, regulatory environment, and AI maturity.

The future of AI depends on the quality and sophistication of the data-centric approach behind it. While AI evolves rapidly and organizations race toward the next big invention, long-term success will depend on proactive data-centric readiness.

Download the white paper to help you keep pace with disruptive AI change, align your strategies with your AI goals and treat data as a product, shifting from reactive data management to forward-looking, scalable data-centric AI practices.

Our data and AI experts can help you establish the right foundations to operationalize AI enterprise-wide with confidence and control.